Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations19768
Missing cells10929
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory220.2 B

Variable types

Categorical1
Boolean6
Text1
Numeric9
DateTime2

Alerts

followers is highly overall correlated with following and 6 other fieldsHigh correlation
following is highly overall correlated with followers and 4 other fieldsHigh correlation
label is highly overall correlated with text_bot_countHigh correlation
log_followers is highly overall correlated with followers and 6 other fieldsHigh correlation
log_following is highly overall correlated with followers and 4 other fieldsHigh correlation
log_public_gists is highly overall correlated with followers and 4 other fieldsHigh correlation
log_public_repos is highly overall correlated with followers and 6 other fieldsHigh correlation
public_gists is highly overall correlated with followers and 4 other fieldsHigh correlation
public_repos is highly overall correlated with followers and 6 other fieldsHigh correlation
text_bot_count is highly overall correlated with label and 1 other fieldsHigh correlation
type is highly overall correlated with text_bot_countHigh correlation
label is highly imbalanced (67.2%) Imbalance
type is highly imbalanced (92.8%) Imbalance
site_admin is highly imbalanced (95.8%) Imbalance
bio has 10929 (55.3%) missing values Missing
public_repos is highly skewed (γ1 = 53.8847472) Skewed
public_gists is highly skewed (γ1 = 74.09063706) Skewed
followers is highly skewed (γ1 = 32.46602776) Skewed
following is highly skewed (γ1 = 39.87415424) Skewed
public_repos has 942 (4.8%) zeros Zeros
public_gists has 7961 (40.3%) zeros Zeros
followers has 1445 (7.3%) zeros Zeros
following has 6017 (30.4%) zeros Zeros
text_bot_count has 19003 (96.1%) zeros Zeros
log_public_repos has 942 (4.8%) zeros Zeros
log_public_gists has 7961 (40.3%) zeros Zeros
log_followers has 1445 (7.3%) zeros Zeros
log_following has 6017 (30.4%) zeros Zeros

Reproduction

Analysis started2024-12-03 10:24:51.056297
Analysis finished2024-12-03 10:24:58.389118
Duration7.33 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

label
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Human
18578 
Bot
 
1190

Length

Max length5
Median length5
Mean length4.8796034
Min length3

Characters and Unicode

Total characters96460
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHuman
2nd rowHuman
3rd rowHuman
4th rowBot
5th rowHuman

Common Values

ValueCountFrequency (%)
Human 18578
94.0%
Bot 1190
 
6.0%

Length

2024-12-03T18:24:58.456548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T18:24:58.531808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
human 18578
94.0%
bot 1190
 
6.0%

Most occurring characters

ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76692
79.5%
Uppercase Letter 19768
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 18578
24.2%
m 18578
24.2%
a 18578
24.2%
n 18578
24.2%
o 1190
 
1.6%
t 1190
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
H 18578
94.0%
B 1190
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

type
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
19597 
False
 
171
ValueCountFrequency (%)
True 19597
99.1%
False 171
 
0.9%
2024-12-03T18:24:58.589528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

site_admin
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
19678 
True
 
90
ValueCountFrequency (%)
False 19678
99.5%
True 90
 
0.5%
2024-12-03T18:24:58.649415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

company
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
10794 
False
8974 
ValueCountFrequency (%)
True 10794
54.6%
False 8974
45.4%
2024-12-03T18:24:58.708261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

blog
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
11256 
True
8512 
ValueCountFrequency (%)
False 11256
56.9%
True 8512
43.1%
2024-12-03T18:24:58.772113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

location
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
12691 
False
7077 
ValueCountFrequency (%)
True 12691
64.2%
False 7077
35.8%
2024-12-03T18:24:58.831194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

hireable
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
16470 
True
3298 
ValueCountFrequency (%)
False 16470
83.3%
True 3298
 
16.7%
2024-12-03T18:24:58.893116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

bio
Text

Missing 

Distinct8641
Distinct (%)97.8%
Missing10929
Missing (%)55.3%
Memory size1.6 MiB
2024-12-03T18:24:59.152305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length160
Median length116
Mean length61.790135
Min length1

Characters and Unicode

Total characters546163
Distinct characters1747
Distinct categories23 ?
Distinct scripts18 ?
Distinct blocks45 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8574 ?
Unique (%)97.0%

Sample

1st rowI just press the buttons randomly, and the program evolves...
2nd rowTime is unimportant, only life important.
3rd rowDone studying. Need challenges.
4th rowAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.
5th rowSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.
ValueCountFrequency (%)
3069
 
3.9%
and 2526
 
3.2%
engineer 1583
 
2.0%
software 1521
 
1.9%
of 1488
 
1.9%
at 1380
 
1.8%
developer 1236
 
1.6%
the 1086
 
1.4%
a 1038
 
1.3%
i 1033
 
1.3%
Other values (14754) 62407
79.6%
2024-12-03T18:24:59.544042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70014
 
12.8%
e 49589
 
9.1%
o 32360
 
5.9%
n 31402
 
5.7%
a 31366
 
5.7%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.2%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1737) 206108
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 388595
71.2%
Space Separator 70192
 
12.9%
Uppercase Letter 43745
 
8.0%
Other Punctuation 23761
 
4.4%
Control 5828
 
1.1%
Decimal Number 3557
 
0.7%
Dash Punctuation 2560
 
0.5%
Other Letter 2016
 
0.4%
Other Symbol 2014
 
0.4%
Math Symbol 1750
 
0.3%
Other values (13) 2145
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
的 25
 
1.2%
不 20
 
1.0%
一 20
 
1.0%
人 14
 
0.7%
程 13
 
0.6%
者 13
 
0.6%
码 12
 
0.6%
の 11
 
0.5%
し 11
 
0.5%
行 11
 
0.5%
Other values (912) 1866
92.6%
Other Symbol
ValueCountFrequency (%)
▄ 141
 
7.0%
💻 86
 
4.3%
🍕 81
 
4.0%
❤ 71
 
3.5%
█ 62
 
3.1%
👨 58
 
2.9%
✨ 58
 
2.9%
🚀 46
 
2.3%
🐁 39
 
1.9%
▀ 39
 
1.9%
Other values (429) 1333
66.2%
Lowercase Letter
ValueCountFrequency (%)
e 49589
12.8%
o 32360
 
8.3%
n 31402
 
8.1%
a 31366
 
8.1%
t 31195
 
8.0%
r 31181
 
8.0%
i 28526
 
7.3%
s 19655
 
5.1%
l 14767
 
3.8%
c 14228
 
3.7%
Other values (137) 104326
26.8%
Nonspacing Mark
ValueCountFrequency (%)
️ 204
60.7%
̶ 10
 
3.0%
̭ 6
 
1.8%
̯ 6
 
1.8%
͉ 6
 
1.8%
͡ 6
 
1.8%
́ 5
 
1.5%
͜ 4
 
1.2%
̪ 4
 
1.2%
̘ 4
 
1.2%
Other values (45) 81
 
24.1%
Uppercase Letter
ValueCountFrequency (%)
S 5685
13.0%
C 3807
 
8.7%
E 3010
 
6.9%
I 2927
 
6.7%
P 2841
 
6.5%
D 2744
 
6.3%
A 2743
 
6.3%
M 2331
 
5.3%
T 2121
 
4.8%
F 1734
 
4.0%
Other values (34) 13802
31.6%
Other Punctuation
ValueCountFrequency (%)
. 7699
32.4%
, 5911
24.9%
@ 4168
17.5%
/ 2005
 
8.4%
: 865
 
3.6%
' 750
 
3.2%
& 663
 
2.8%
! 383
 
1.6%
# 310
 
1.3%
• 221
 
0.9%
Other values (24) 786
 
3.3%
Math Symbol
ValueCountFrequency (%)
| 1137
65.0%
+ 407
 
23.3%
> 70
 
4.0%
= 43
 
2.5%
< 39
 
2.2%
~ 26
 
1.5%
→ 8
 
0.5%
← 4
 
0.2%
⋅ 3
 
0.2%
≖ 2
 
0.1%
Other values (10) 11
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 659
18.5%
0 591
16.6%
1 581
16.3%
3 361
10.1%
9 268
7.5%
8 240
 
6.7%
6 234
 
6.6%
4 224
 
6.3%
5 216
 
6.1%
7 179
 
5.0%
Other values (3) 4
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 570
84.9%
] 58
 
8.6%
} 18
 
2.7%
） 9
 
1.3%
❯ 5
 
0.7%
」 4
 
0.6%
】 3
 
0.4%
〉 2
 
0.3%
》 1
 
0.1%
༽ 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 536
85.1%
[ 57
 
9.0%
{ 19
 
3.0%
（ 7
 
1.1%
【 3
 
0.5%
「 2
 
0.3%
︵ 2
 
0.3%
„ 2
 
0.3%
《 1
 
0.2%
༼ 1
 
0.2%
Modifier Symbol
ValueCountFrequency (%)
🏻 30
34.5%
¯ 16
18.4%
` 14
16.1%
🏽 10
 
11.5%
🏼 9
 
10.3%
^ 3
 
3.4%
🏾 3
 
3.4%
￣ 2
 
2.3%
Private Use
ValueCountFrequency (%)
6
40.0%
2
 
13.3%
2
 
13.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Space Separator
ValueCountFrequency (%)
70014
99.7%
  61
 
0.1%
  48
 
0.1%
　 40
 
0.1%
  27
 
< 0.1%
  2
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
ー 10
62.5%
ˈ 2
 
12.5%
ˌ 2
 
12.5%
ₑ 1
 
6.2%
ː 1
 
6.2%
Other Number
ValueCountFrequency (%)
² 2
33.3%
⁵ 1
16.7%
⁰ 1
16.7%
¹ 1
16.7%
¼ 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 2511
98.1%
— 30
 
1.2%
– 18
 
0.7%
― 1
 
< 0.1%
Format
ValueCountFrequency (%)
‍ 142
96.6%
​ 2
 
1.4%
­ 2
 
1.4%
‎ 1
 
0.7%
Final Punctuation
ValueCountFrequency (%)
’ 29
60.4%
” 14
29.2%
» 5
 
10.4%
Currency Symbol
ValueCountFrequency (%)
$ 13
68.4%
₿ 5
 
26.3%
£ 1
 
5.3%
Initial Punctuation
ValueCountFrequency (%)
“ 12
70.6%
‘ 4
 
23.5%
« 1
 
5.9%
Control
ValueCountFrequency (%)
2914
50.0%
2914
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 147
96.7%
‿ 5
 
3.3%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 432048
79.1%
Common 111333
 
20.4%
Han 1521
 
0.3%
Inherited 478
 
0.1%
Cyrillic 244
 
< 0.1%
Hangul 174
 
< 0.1%
Hiragana 155
 
< 0.1%
Katakana 79
 
< 0.1%
Arabic 67
 
< 0.1%
Greek 26
 
< 0.1%
Other values (8) 38
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
的 25
 
1.6%
不 20
 
1.3%
一 20
 
1.3%
人 14
 
0.9%
程 13
 
0.9%
者 13
 
0.9%
码 12
 
0.8%
行 11
 
0.7%
以 11
 
0.7%
有 11
 
0.7%
Other values (680) 1371
90.1%
Common
ValueCountFrequency (%)
70014
62.9%
. 7699
 
6.9%
, 5911
 
5.3%
@ 4168
 
3.7%
2914
 
2.6%
2914
 
2.6%
- 2511
 
2.3%
/ 2005
 
1.8%
| 1137
 
1.0%
: 865
 
0.8%
Other values (575) 11195
 
10.1%
Latin
ValueCountFrequency (%)
e 49589
 
11.5%
o 32360
 
7.5%
n 31402
 
7.3%
a 31366
 
7.3%
t 31195
 
7.2%
r 31181
 
7.2%
i 28526
 
6.6%
s 19655
 
4.5%
l 14767
 
3.4%
c 14228
 
3.3%
Other values (107) 147779
34.2%
Hangul
ValueCountFrequency (%)
ㅤ 8
 
4.6%
다 7
 
4.0%
는 7
 
4.0%
발 5
 
2.9%
을 4
 
2.3%
이 4
 
2.3%
니 4
 
2.3%
자 4
 
2.3%
드 4
 
2.3%
하 3
 
1.7%
Other values (102) 124
71.3%
Inherited
ValueCountFrequency (%)
️ 204
42.7%
‍ 142
29.7%
̶ 10
 
2.1%
̭ 6
 
1.3%
̯ 6
 
1.3%
͉ 6
 
1.3%
͡ 6
 
1.3%
́ 5
 
1.0%
͜ 4
 
0.8%
̪ 4
 
0.8%
Other values (46) 85
17.8%
Katakana
ValueCountFrequency (%)
ツ 10
 
12.7%
ム 4
 
5.1%
コ 4
 
5.1%
ン 3
 
3.8%
ｧ 3
 
3.8%
ロ 3
 
3.8%
ラ 3
 
3.8%
ル 3
 
3.8%
ゲ 3
 
3.8%
ィ 2
 
2.5%
Other values (32) 41
51.9%
Cyrillic
ValueCountFrequency (%)
а 27
 
11.1%
о 18
 
7.4%
т 18
 
7.4%
н 14
 
5.7%
е 13
 
5.3%
и 12
 
4.9%
в 12
 
4.9%
с 11
 
4.5%
у 9
 
3.7%
р 8
 
3.3%
Other values (31) 102
41.8%
Hiragana
ValueCountFrequency (%)
の 11
 
7.1%
し 11
 
7.1%
す 8
 
5.2%
い 7
 
4.5%
ま 7
 
4.5%
を 7
 
4.5%
た 7
 
4.5%
て 6
 
3.9%
は 6
 
3.9%
と 6
 
3.9%
Other values (30) 79
51.0%
Arabic
ValueCountFrequency (%)
ا 10
14.9%
م 8
11.9%
و 7
10.4%
ت 6
 
9.0%
ل 5
 
7.5%
ع 4
 
6.0%
ر 4
 
6.0%
ة 3
 
4.5%
ي 3
 
4.5%
خ 2
 
3.0%
Other values (12) 15
22.4%
Greek
ValueCountFrequency (%)
ω 4
15.4%
λ 3
11.5%
ρ 2
 
7.7%
ς 2
 
7.7%
θ 2
 
7.7%
π 2
 
7.7%
η 1
 
3.8%
Θ 1
 
3.8%
ο 1
 
3.8%
δ 1
 
3.8%
Other values (7) 7
26.9%
Hebrew
ValueCountFrequency (%)
מ 2
14.3%
ר 2
14.3%
ש 2
14.3%
ו 1
7.1%
א 1
7.1%
ל 1
7.1%
ע 1
7.1%
ה 1
7.1%
ח 1
7.1%
י 1
7.1%
Unknown
ValueCountFrequency (%)
6
40.0%
2
 
13.3%
2
 
13.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Tibetan
ValueCountFrequency (%)
༼ 1
50.0%
༽ 1
50.0%
Thai
ValueCountFrequency (%)
๑ 2
100.0%
Kannada
ValueCountFrequency (%)
ಠ 2
100.0%
Mandaic
ValueCountFrequency (%)
ࡇ 1
100.0%
Egyptian_Hieroglyphs
ValueCountFrequency (%)
𓀡 1
100.0%
Devanagari
ValueCountFrequency (%)
ॐ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540182
98.9%
None 1839
 
0.3%
CJK 1521
 
0.3%
Punctuation 576
 
0.1%
Block Elements 255
 
< 0.1%
Cyrillic 244
 
< 0.1%
VS 205
 
< 0.1%
Enclosed Alphanum Sup 181
 
< 0.1%
Hangul 165
 
< 0.1%
Dingbats 160
 
< 0.1%
Other values (35) 835
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70014
 
13.0%
e 49589
 
9.2%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.8%
r 31181
 
5.8%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (87) 200127
37.0%
Punctuation
ValueCountFrequency (%)
• 221
38.4%
‍ 142
24.7%
  48
 
8.3%
— 30
 
5.2%
’ 29
 
5.0%
  27
 
4.7%
– 18
 
3.1%
” 14
 
2.4%
“ 12
 
2.1%
… 12
 
2.1%
Other values (10) 23
 
4.0%
VS
ValueCountFrequency (%)
️ 204
99.5%
︎ 1
 
0.5%
Block Elements
ValueCountFrequency (%)
▄ 141
55.3%
█ 62
24.3%
▀ 39
 
15.3%
░ 11
 
4.3%
▐ 1
 
0.4%
▌ 1
 
0.4%
None
ValueCountFrequency (%)
💻 86
 
4.7%
🍕 81
 
4.4%
， 73
 
4.0%
  61
 
3.3%
👨 58
 
3.2%
。 55
 
3.0%
· 54
 
2.9%
🚀 46
 
2.5%
　 40
 
2.2%
🐁 39
 
2.1%
Other values (407) 1246
67.8%
Dingbats
ValueCountFrequency (%)
❤ 71
44.4%
✨ 58
36.2%
❯ 5
 
3.1%
✌ 5
 
3.1%
✍ 3
 
1.9%
❄ 2
 
1.2%
✊ 2
 
1.2%
✝ 2
 
1.2%
✿ 1
 
0.6%
❞ 1
 
0.6%
Other values (10) 10
 
6.2%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇦 31
17.1%
🇺 29
16.0%
🇧 17
9.4%
🇨 15
8.3%
🇷 15
8.3%
🇬 11
 
6.1%
🇪 8
 
4.4%
🇸 8
 
4.4%
🇹 6
 
3.3%
🇾 6
 
3.3%
Other values (13) 35
19.3%
Misc Symbols
ValueCountFrequency (%)
♂ 30
21.9%
⚡ 21
15.3%
☕ 14
10.2%
♥ 12
 
8.8%
☁ 9
 
6.6%
⚛ 5
 
3.6%
⚙ 4
 
2.9%
⚧ 4
 
2.9%
⚫ 3
 
2.2%
♀ 3
 
2.2%
Other values (24) 32
23.4%
Cyrillic
ValueCountFrequency (%)
а 27
 
11.1%
о 18
 
7.4%
т 18
 
7.4%
н 14
 
5.7%
е 13
 
5.3%
и 12
 
4.9%
в 12
 
4.9%
с 11
 
4.5%
у 9
 
3.7%
р 8
 
3.3%
Other values (31) 102
41.8%
CJK
ValueCountFrequency (%)
的 25
 
1.6%
不 20
 
1.3%
一 20
 
1.3%
人 14
 
0.9%
程 13
 
0.9%
者 13
 
0.9%
码 12
 
0.8%
行 11
 
0.7%
以 11
 
0.7%
有 11
 
0.7%
Other values (680) 1371
90.1%
Hiragana
ValueCountFrequency (%)
の 11
 
7.1%
し 11
 
7.1%
す 8
 
5.2%
い 7
 
4.5%
ま 7
 
4.5%
を 7
 
4.5%
た 7
 
4.5%
て 6
 
3.9%
は 6
 
3.9%
と 6
 
3.9%
Other values (30) 79
51.0%
Arabic
ValueCountFrequency (%)
ا 10
14.7%
م 8
11.8%
و 7
10.3%
ت 6
 
8.8%
ل 5
 
7.4%
ع 4
 
5.9%
ر 4
 
5.9%
ة 3
 
4.4%
ي 3
 
4.4%
خ 2
 
2.9%
Other values (13) 16
23.5%
Katakana
ValueCountFrequency (%)
ー 10
 
11.9%
ツ 10
 
11.9%
・ 5
 
6.0%
ム 4
 
4.8%
コ 4
 
4.8%
ン 3
 
3.6%
ロ 3
 
3.6%
ラ 3
 
3.6%
ル 3
 
3.6%
ゲ 3
 
3.6%
Other values (27) 36
42.9%
Diacriticals
ValueCountFrequency (%)
̶ 10
 
7.8%
̭ 6
 
4.7%
̯ 6
 
4.7%
͉ 6
 
4.7%
͡ 6
 
4.7%
́ 5
 
3.9%
͜ 4
 
3.1%
̪ 4
 
3.1%
̘ 4
 
3.1%
̩ 4
 
3.1%
Other values (40) 73
57.0%
Geometric Shapes Ext
ValueCountFrequency (%)
🟦 8
57.1%
🟨 6
42.9%
Arrows
ValueCountFrequency (%)
→ 8
53.3%
← 4
26.7%
⇋ 3
 
20.0%
Compat Jamo
ValueCountFrequency (%)
ㅤ 8
100.0%
Hangul
ValueCountFrequency (%)
다 7
 
4.2%
는 7
 
4.2%
발 5
 
3.0%
을 4
 
2.4%
이 4
 
2.4%
니 4
 
2.4%
자 4
 
2.4%
드 4
 
2.4%
하 3
 
1.8%
개 3
 
1.8%
Other values (100) 120
72.7%
Emoticons
ValueCountFrequency (%)
🙈 7
 
11.9%
🙉 6
 
10.2%
😄 4
 
6.8%
🙂 4
 
6.8%
😎 3
 
5.1%
😁 2
 
3.4%
🙋 2
 
3.4%
😋 2
 
3.4%
😜 2
 
3.4%
🙊 2
 
3.4%
Other values (18) 25
42.4%
Box Drawing
ValueCountFrequency (%)
╯ 6
28.6%
┻ 6
28.6%
║ 5
23.8%
━ 3
14.3%
╬ 1
 
4.8%
PUA
ValueCountFrequency (%)
6
40.0%
2
 
13.3%
2
 
13.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Geometric Shapes
ValueCountFrequency (%)
◕ 6
31.6%
◉ 2
 
10.5%
▽ 2
 
10.5%
○ 2
 
10.5%
▲ 2
 
10.5%
□ 2
 
10.5%
● 1
 
5.3%
◦ 1
 
5.3%
▸ 1
 
5.3%
Letterlike Symbols
ValueCountFrequency (%)
™ 5
100.0%
Currency Symbols
ValueCountFrequency (%)
₿ 5
100.0%
Sup Punctuation
ValueCountFrequency (%)
⸱ 4
100.0%
Math Operators
ValueCountFrequency (%)
⋅ 3
23.1%
≖ 2
15.4%
∠ 2
15.4%
≦ 1
 
7.7%
≧ 1
 
7.7%
∀ 1
 
7.7%
⊂ 1
 
7.7%
≺ 1
 
7.7%
⊃ 1
 
7.7%
Phonetic Ext
ValueCountFrequency (%)
ᴥ 2
33.3%
ᴏ 1
16.7%
ᴛ 1
16.7%
ᴇ 1
16.7%
ᴍ 1
16.7%
Thai
ValueCountFrequency (%)
๑ 2
100.0%
Modifier Letters
ValueCountFrequency (%)
ˈ 2
40.0%
ˌ 2
40.0%
ː 1
20.0%
Math Alphanum
ValueCountFrequency (%)
𝘶 2
 
8.3%
𝘭 2
 
8.3%
𝘴 2
 
8.3%
𝘵 2
 
8.3%
𝒽 1
 
4.2%
𝒾 1
 
4.2%
𝐂 1
 
4.2%
𝐑 1
 
4.2%
𝟎 1
 
4.2%
𝟖 1
 
4.2%
Other values (10) 10
41.7%
IPA Ext
ValueCountFrequency (%)
ʖ 2
20.0%
ʕ 1
10.0%
ʔ 1
10.0%
ʀ 1
10.0%
ɴ 1
10.0%
ɾ 1
10.0%
ɚ 1
10.0%
ɹ 1
10.0%
ɛ 1
10.0%
Hebrew
ValueCountFrequency (%)
מ 2
14.3%
ר 2
14.3%
ש 2
14.3%
ו 1
7.1%
א 1
7.1%
ל 1
7.1%
ע 1
7.1%
ה 1
7.1%
ח 1
7.1%
י 1
7.1%
Misc Technical
ValueCountFrequency (%)
⎈ 2
28.6%
⌚ 1
14.3%
⏱ 1
14.3%
⌁ 1
14.3%
⌘ 1
14.3%
⏳ 1
14.3%
CJK Compat Forms
ValueCountFrequency (%)
︵ 2
100.0%
Kannada
ValueCountFrequency (%)
ಠ 2
100.0%
Diacriticals Sup
ValueCountFrequency (%)
᷄ 1
50.0%
᷅ 1
50.0%
Mandaic
ValueCountFrequency (%)
ࡇ 1
100.0%
Jamo
ValueCountFrequency (%)
ᅟ 1
100.0%
Mahjong
ValueCountFrequency (%)
🀄 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%
Latin Ext Additional
ValueCountFrequency (%)
ờ 1
50.0%
ṃ 1
50.0%
Egyptian Hieroglyphs
ValueCountFrequency (%)
𓀡 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
Ⓐ 1
100.0%
Tibetan
ValueCountFrequency (%)
༼ 1
50.0%
༽ 1
50.0%
Devanagari
ValueCountFrequency (%)
ॐ 1
100.0%

public_repos
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct674
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.139215
Minimum0
Maximum50000
Zeros942
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-12-03T18:24:59.642209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median35
Q383
95-th percentile250
Maximum50000
Range50000
Interquartile range (IQR)72

Descriptive statistics

Standard deviation574.75022
Coefficient of variation (CV)6.8309434
Kurtosis3700.1203
Mean84.139215
Median Absolute Deviation (MAD)29
Skewness53.884747
Sum1663264
Variance330337.81
MonotonicityNot monotonic
2024-12-03T18:24:59.726141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942
 
4.8%
1 551
 
2.8%
2 465
 
2.4%
3 396
 
2.0%
4 380
 
1.9%
6 364
 
1.8%
5 357
 
1.8%
7 330
 
1.7%
9 312
 
1.6%
8 307
 
1.6%
Other values (664) 15364
77.7%
ValueCountFrequency (%)
0 942
4.8%
1 551
2.8%
2 465
2.4%
3 396
2.0%
4 380
1.9%
5 357
 
1.8%
6 364
 
1.8%
7 330
 
1.7%
8 307
 
1.6%
9 312
 
1.6%
ValueCountFrequency (%)
50000 1
< 0.1%
27746 1
< 0.1%
26360 1
< 0.1%
22618 1
< 0.1%
20693 1
< 0.1%
17425 1
< 0.1%
16985 1
< 0.1%
16839 1
< 0.1%
9666 1
< 0.1%
9554 1
< 0.1%

public_gists
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct359
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.214083
Minimum0
Maximum55781
Zeros7961
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-12-03T18:24:59.808093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile66
Maximum55781
Range55781
Interquartile range (IQR)10

Descriptive statistics

Standard deviation635.69014
Coefficient of variation (CV)25.211709
Kurtosis5955.7935
Mean25.214083
Median Absolute Deviation (MAD)2
Skewness74.090637
Sum498432
Variance404101.96
MonotonicityNot monotonic
2024-12-03T18:24:59.890045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7961
40.3%
1 1873
 
9.5%
2 1152
 
5.8%
3 823
 
4.2%
4 665
 
3.4%
5 627
 
3.2%
6 488
 
2.5%
7 405
 
2.0%
9 327
 
1.7%
8 318
 
1.6%
Other values (349) 5129
25.9%
ValueCountFrequency (%)
0 7961
40.3%
1 1873
 
9.5%
2 1152
 
5.8%
3 823
 
4.2%
4 665
 
3.4%
5 627
 
3.2%
6 488
 
2.5%
7 405
 
2.0%
8 318
 
1.6%
9 327
 
1.7%
ValueCountFrequency (%)
55781 1
< 0.1%
53660 1
< 0.1%
28943 1
< 0.1%
26879 1
< 0.1%
15482 1
< 0.1%
10604 1
< 0.1%
3450 1
< 0.1%
3170 1
< 0.1%
2565 1
< 0.1%
1750 1
< 0.1%

followers
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1598
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.49702
Minimum0
Maximum95752
Zeros1445
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-12-03T18:24:59.975032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median33
Q3125
95-th percentile836
Maximum95752
Range95752
Interquartile range (IQR)118

Descriptive statistics

Standard deviation1535.94
Coefficient of variation (CV)6.2564506
Kurtosis1570.3008
Mean245.49702
Median Absolute Deviation (MAD)31
Skewness32.466028
Sum4852985
Variance2359111.6
MonotonicityNot monotonic
2024-12-03T18:25:00.064187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1445
 
7.3%
1 803
 
4.1%
2 623
 
3.2%
3 515
 
2.6%
4 450
 
2.3%
5 415
 
2.1%
6 396
 
2.0%
7 347
 
1.8%
8 338
 
1.7%
9 311
 
1.6%
Other values (1588) 14125
71.5%
ValueCountFrequency (%)
0 1445
7.3%
1 803
4.1%
2 623
3.2%
3 515
 
2.6%
4 450
 
2.3%
5 415
 
2.1%
6 396
 
2.0%
7 347
 
1.8%
8 338
 
1.7%
9 311
 
1.6%
ValueCountFrequency (%)
95752 1
< 0.1%
84979 1
< 0.1%
66203 1
< 0.1%
58452 1
< 0.1%
31120 1
< 0.1%
30287 1
< 0.1%
29719 1
< 0.1%
29414 1
< 0.1%
28411 1
< 0.1%
25815 1
< 0.1%

following
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.520741
Minimum0
Maximum27775
Zeros6017
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-12-03T18:25:00.145518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q322
95-th percentile148
Maximum27775
Range27775
Interquartile range (IQR)22

Descriptive statistics

Standard deviation366.79344
Coefficient of variation (CV)8.2387093
Kurtosis2260.6155
Mean44.520741
Median Absolute Deviation (MAD)4
Skewness39.874154
Sum880086
Variance134537.43
MonotonicityNot monotonic
2024-12-03T18:25:00.223673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6017
30.4%
1 1734
 
8.8%
2 1092
 
5.5%
3 794
 
4.0%
4 602
 
3.0%
5 533
 
2.7%
6 484
 
2.4%
7 407
 
2.1%
8 368
 
1.9%
9 322
 
1.6%
Other values (610) 7415
37.5%
ValueCountFrequency (%)
0 6017
30.4%
1 1734
 
8.8%
2 1092
 
5.5%
3 794
 
4.0%
4 602
 
3.0%
5 533
 
2.7%
6 484
 
2.4%
7 407
 
2.1%
8 368
 
1.9%
9 322
 
1.6%
ValueCountFrequency (%)
27775 1
< 0.1%
16741 1
< 0.1%
15931 1
< 0.1%
11921 1
< 0.1%
10268 1
< 0.1%
9720 1
< 0.1%
9686 1
< 0.1%
9532 1
< 0.1%
9367 1
< 0.1%
7374 1
< 0.1%

created_at
Date

Distinct19767
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Minimum2008-01-27 07:09:47+00:00
Maximum2021-12-20 05:29:41+00:00
2024-12-03T18:25:00.309175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:25:00.399233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

updated_at
Date

Distinct19633
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Minimum2016-08-08 22:18:09+00:00
Maximum2023-10-14 14:33:48+00:00
2024-12-03T18:25:00.490765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:25:00.590284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

text_bot_count
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061361797
Minimum0
Maximum5
Zeros19003
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2024-12-03T18:25:00.663583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34100309
Coefficient of variation (CV)5.5572539
Kurtosis51.672415
Mean0.061361797
Median Absolute Deviation (MAD)0
Skewness6.674794
Sum1213
Variance0.11628311
MonotonicityNot monotonic
2024-12-03T18:25:00.733233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19003
96.1%
1 425
 
2.1%
2 251
 
1.3%
3 75
 
0.4%
4 9
 
< 0.1%
5 5
 
< 0.1%
ValueCountFrequency (%)
0 19003
96.1%
1 425
 
2.1%
2 251
 
1.3%
3 75
 
0.4%
4 9
 
< 0.1%
5 5
 
< 0.1%
ValueCountFrequency (%)
5 5
 
< 0.1%
4 9
 
< 0.1%
3 75
 
0.4%
2 251
 
1.3%
1 425
 
2.1%
0 19003
96.1%

log_public_repos
Real number (ℝ)

High correlation  Zeros 

Distinct674
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3934449
Minimum0
Maximum10.819798
Zeros942
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-12-03T18:25:00.817719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.69314718
Q12.4849066
median3.5835189
Q34.4308168
95-th percentile5.5254529
Maximum10.819798
Range10.819798
Interquartile range (IQR)1.9459101

Descriptive statistics

Standard deviation1.4801216
Coefficient of variation (CV)0.4361708
Kurtosis0.063401207
Mean3.3934449
Median Absolute Deviation (MAD)0.94446161
Skewness-0.38244821
Sum67081.618
Variance2.1907598
MonotonicityNot monotonic
2024-12-03T18:25:00.909769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942
 
4.8%
0.6931471806 551
 
2.8%
1.098612289 465
 
2.4%
1.386294361 396
 
2.0%
1.609437912 380
 
1.9%
1.945910149 364
 
1.8%
1.791759469 357
 
1.8%
2.079441542 330
 
1.7%
2.302585093 312
 
1.6%
2.197224577 307
 
1.6%
Other values (664) 15364
77.7%
ValueCountFrequency (%)
0 942
4.8%
0.6931471806 551
2.8%
1.098612289 465
2.4%
1.386294361 396
2.0%
1.609437912 380
1.9%
1.791759469 357
 
1.8%
1.945910149 364
 
1.8%
2.079441542 330
 
1.7%
2.197224577 307
 
1.6%
2.302585093 312
 
1.6%
ValueCountFrequency (%)
10.81979828 1
< 0.1%
10.23088301 1
< 0.1%
10.17964092 1
< 0.1%
10.02654554 1
< 0.1%
9.937599082 1
< 0.1%
9.765718623 1
< 0.1%
9.740144754 1
< 0.1%
9.731512288 1
< 0.1%
9.176473302 1
< 0.1%
9.164819857 1
< 0.1%

log_public_gists
Real number (ℝ)

High correlation  Zeros 

Distinct359
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3667909
Minimum0
Maximum10.929207
Zeros7961
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-12-03T18:25:00.999001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.0986123
Q32.3978953
95-th percentile4.2046926
Maximum10.929207
Range10.929207
Interquartile range (IQR)2.3978953

Descriptive statistics

Standard deviation1.4937885
Coefficient of variation (CV)1.0929166
Kurtosis0.26107473
Mean1.3667909
Median Absolute Deviation (MAD)1.0986123
Skewness0.93069164
Sum27018.723
Variance2.231404
MonotonicityNot monotonic
2024-12-03T18:25:01.087520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7961
40.3%
0.6931471806 1873
 
9.5%
1.098612289 1152
 
5.8%
1.386294361 823
 
4.2%
1.609437912 665
 
3.4%
1.791759469 627
 
3.2%
1.945910149 488
 
2.5%
2.079441542 405
 
2.0%
2.302585093 327
 
1.7%
2.197224577 318
 
1.6%
Other values (349) 5129
25.9%
ValueCountFrequency (%)
0 7961
40.3%
0.6931471806 1873
 
9.5%
1.098612289 1152
 
5.8%
1.386294361 823
 
4.2%
1.609437912 665
 
3.4%
1.791759469 627
 
3.2%
1.945910149 488
 
2.5%
2.079441542 405
 
2.0%
2.197224577 318
 
1.6%
2.302585093 327
 
1.7%
ValueCountFrequency (%)
10.92920652 1
< 0.1%
10.89044176 1
< 0.1%
10.27311821 1
< 0.1%
10.19913779 1
< 0.1%
9.647497927 1
< 0.1%
9.269080867 1
< 0.1%
8.146419323 1
< 0.1%
8.061802275 1
< 0.1%
7.850103545 1
< 0.1%
7.467942332 1
< 0.1%

log_followers
Real number (ℝ)

High correlation  Zeros 

Distinct1598
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5025164
Minimum0
Maximum11.469527
Zeros1445
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-12-03T18:25:01.427490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.0794415
median3.5263605
Q34.8362819
95-th percentile6.7298241
Maximum11.469527
Range11.469527
Interquartile range (IQR)2.7568404

Descriptive statistics

Standard deviation1.9557633
Coefficient of variation (CV)0.55838804
Kurtosis-0.29389155
Mean3.5025164
Median Absolute Deviation (MAD)1.3291359
Skewness0.12973968
Sum69237.744
Variance3.8250099
MonotonicityNot monotonic
2024-12-03T18:25:01.515321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1445
 
7.3%
0.6931471806 803
 
4.1%
1.098612289 623
 
3.2%
1.386294361 515
 
2.6%
1.609437912 450
 
2.3%
1.791759469 415
 
2.1%
1.945910149 396
 
2.0%
2.079441542 347
 
1.8%
2.197224577 338
 
1.7%
2.302585093 311
 
1.6%
Other values (1588) 14125
71.5%
ValueCountFrequency (%)
0 1445
7.3%
0.6931471806 803
4.1%
1.098612289 623
3.2%
1.386294361 515
 
2.6%
1.609437912 450
 
2.3%
1.791759469 415
 
2.1%
1.945910149 396
 
2.0%
2.079441542 347
 
1.8%
2.197224577 338
 
1.7%
2.302585093 311
 
1.6%
ValueCountFrequency (%)
11.46952724 1
< 0.1%
11.35017121 1
< 0.1%
11.10049616 1
< 0.1%
10.97597829 1
< 0.1%
10.34563811 1
< 0.1%
10.31850687 1
< 0.1%
10.2995755 1
< 0.1%
10.28926003 1
< 0.1%
10.25456687 1
< 0.1%
10.15874973 1
< 0.1%

log_following
Real number (ℝ)

High correlation  Zeros 

Distinct620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8589591
Minimum0
Maximum10.231928
Zeros6017
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-12-03T18:25:01.604320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.6094379
Q33.1354942
95-th percentile5.0039463
Maximum10.231928
Range10.231928
Interquartile range (IQR)3.1354942

Descriptive statistics

Standard deviation1.743082
Coefficient of variation (CV)0.93766562
Kurtosis-0.25441172
Mean1.8589591
Median Absolute Deviation (MAD)1.6094379
Skewness0.68128993
Sum36747.903
Variance3.0383349
MonotonicityNot monotonic
2024-12-03T18:25:01.688400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6017
30.4%
0.6931471806 1734
 
8.8%
1.098612289 1092
 
5.5%
1.386294361 794
 
4.0%
1.609437912 602
 
3.0%
1.791759469 533
 
2.7%
1.945910149 484
 
2.4%
2.079441542 407
 
2.1%
2.197224577 368
 
1.9%
2.302585093 322
 
1.6%
Other values (610) 7415
37.5%
ValueCountFrequency (%)
0 6017
30.4%
0.6931471806 1734
 
8.8%
1.098612289 1092
 
5.5%
1.386294361 794
 
4.0%
1.609437912 602
 
3.0%
1.791759469 533
 
2.7%
1.945910149 484
 
2.4%
2.079441542 407
 
2.1%
2.197224577 368
 
1.9%
2.302585093 322
 
1.6%
ValueCountFrequency (%)
10.23192762 1
< 0.1%
9.725675811 1
< 0.1%
9.676084944 1
< 0.1%
9.386140712 1
< 0.1%
9.236884927 1
< 0.1%
9.182043773 1
< 0.1%
9.178540059 1
< 0.1%
9.162514742 1
< 0.1%
9.145054905 1
< 0.1%
8.905851181 1
< 0.1%

Interactions

2024-12-03T18:24:57.295379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.391947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.975388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.574759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.171587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.998293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.572659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.151645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.711569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.362084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.456030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.040516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.648839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.237141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.068273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.639524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.215730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.773652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.622253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.521550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.103979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.714530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.307073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.128063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.704167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.281647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.841774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.685295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.584239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.167740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.780354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.378058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.191631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.770969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.347683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.909773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.750074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.655689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.234970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.844287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.440530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.253182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.835288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.410829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.975294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.813554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.717626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.301286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.908416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.509743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.315575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.906834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.471175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.044307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.877069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.782012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.371012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.978263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.801597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.378310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.965714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.532200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.105800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.936440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.843011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.436358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.042114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.867768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.442756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.028728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.593343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.172273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:58.003016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:52.909970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:53.511199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.108258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:54.932710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:55.508739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.090852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:56.654802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-03T18:24:57.232236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-03T18:25:01.752488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
blogcompanyfollowersfollowinghireablelabellocationlog_followerslog_followinglog_public_gistslog_public_repospublic_gistspublic_repossite_admintext_bot_counttype
blog1.0000.2580.0460.0220.2180.0240.3690.4270.3590.3570.3650.0120.0000.0050.0620.080
company0.2581.0000.0180.0050.0570.0700.3920.2590.1960.1800.1980.0000.0100.0250.0690.102
followers0.0460.0181.0000.5370.0000.0000.0201.0000.5370.5970.6510.5970.6510.000-0.1500.000
following0.0220.0050.5371.0000.0410.0000.0000.5371.0000.4380.5370.4380.5370.000-0.1590.000
hireable0.2180.0570.0000.0411.0000.0580.1780.2140.2700.1990.2320.0000.0180.0130.0490.040
label0.0240.0700.0000.0000.0581.0000.1300.1630.1650.1410.3690.0410.0180.0060.5790.368
location0.3690.3920.0200.0000.1780.1301.0000.3980.3590.2880.3570.0000.0000.0190.1310.124
log_followers0.4270.2591.0000.5370.2140.1630.3981.0000.5370.5970.6510.5970.6510.079-0.1500.226
log_following0.3590.1960.5371.0000.2700.1650.3590.5371.0000.4380.5370.4380.5370.000-0.1590.114
log_public_gists0.3570.1800.5970.4380.1990.1410.2880.5970.4381.0000.6361.0000.6360.026-0.1370.091
log_public_repos0.3650.1980.6510.5370.2320.3690.3570.6510.5370.6361.0000.6361.0000.022-0.2040.326
public_gists0.0120.0000.5970.4380.0000.0410.0000.5970.4381.0000.6361.0000.6360.000-0.1370.000
public_repos0.0000.0100.6510.5370.0180.0180.0000.6510.5370.6361.0000.6361.0000.000-0.2040.000
site_admin0.0050.0250.0000.0000.0130.0060.0190.0790.0000.0260.0220.0000.0001.0000.0000.000
text_bot_count0.0620.069-0.150-0.1590.0490.5790.131-0.150-0.159-0.137-0.204-0.137-0.2040.0001.0000.510
type0.0800.1020.0000.0000.0400.3680.1240.2260.1140.0910.3260.0000.0000.0000.5101.000

Missing values

2024-12-03T18:24:58.102377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T18:24:58.283907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
0HumanTrueFalseFalseFalseFalseFalseNaN261512011-09-26 17:27:03+00:002023-10-13 11:21:10+00:0003.2958370.6931471.7917590.693147
1HumanTrueFalseFalseTrueFalseTrueI just press the buttons randomly, and the program evolves...303962015-06-29 10:12:46+00:002023-10-07 06:26:14+00:0003.4339871.3862942.3025851.945910
2HumanTrueFalseTrueTrueTrueTrueTime is unimportant,\r\nonly life important.1034912122212008-08-29 16:20:03+00:002023-10-02 02:11:21+00:0004.6443913.9120237.1008525.402677
3BotTrueFalseFalseFalseTrueFalseNaN4908422014-05-20 18:43:09+00:002023-10-12 12:54:59+00:0003.9120230.0000004.4426511.098612
4HumanTrueFalseFalseFalseFalseTrueNaN111622012-08-16 14:19:13+00:002023-10-06 11:58:41+00:0002.4849070.6931471.9459101.098612
5HumanTrueFalseTrueTrueTrueFalseDone studying. Need challenges.5612272017-04-11 14:08:07+00:002023-10-11 05:59:26+00:0004.0430510.6931473.1354942.079442
6HumanTrueFalseTrueTrueTrueTrueAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.277113963162008-04-07 22:22:22+00:002023-09-27 09:04:56+00:0005.6276217.0387844.1588832.833213
7HumanTrueFalseTrueFalseTrueFalseSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.3712202012-01-19 21:57:07+00:002023-08-07 16:06:34+00:0003.6375860.6931473.1354940.000000
8HumanTrueFalseFalseFalseFalseFalseNaN272375962019-12-24 20:04:33+00:002023-10-12 11:55:01+00:0003.3322051.0986123.6375866.391917
9HumanTrueFalseTrueTrueTrueFalseHi4291422013-07-23 23:29:34+00:002023-10-09 20:47:05+00:0003.7612002.3025852.7080501.098612
labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
19758HumanTrueFalseTrueFalseTrueFalseNaN30010112016-09-10 09:45:00+00:002023-10-06 11:30:51+00:0003.4339870.0000002.3978952.484907
19759HumanTrueFalseFalseFalseTrueTrueNaN37199162012-04-19 03:27:14+00:002023-10-07 18:13:52+00:0003.6375862.9957324.5217891.945910
19760BotTrueFalseFalseFalseFalseFalseI am the bot account of @alvaroaleman10002018-12-15 19:55:31+00:002021-07-27 14:14:25+00:0020.6931470.0000000.0000000.000000
19761HumanTrueFalseFalseFalseFalseFalseNaN30102013-11-10 16:05:37+00:002023-08-31 14:26:08+00:0021.3862940.0000000.6931470.000000
19762HumanTrueFalseFalseFalseFalseFalseNaN00002020-10-01 18:30:32+00:002020-12-29 19:45:12+00:0000.0000000.0000000.0000000.000000
19763BotTrueFalseTrueTrueTrueFalseTony came to Linux in 1994 and has never looked back. His entire professional career has been spent working with or on Linux. First as a systems administrator36161142014-07-02 23:27:34+00:002023-08-15 16:38:34+00:0003.6109182.8332132.4849071.609438
19764HumanTrueFalseFalseFalseFalseFalseNaN160302017-12-06 21:56:31+00:002023-07-26 18:32:25+00:0002.8332130.0000001.3862940.000000
19765HumanTrueFalseTrueFalseTrueFalseSoftware engineer at RealTracs.1301012015-11-14 14:44:05+00:002022-08-23 21:09:49+00:0002.6390570.0000002.3978950.693147
19766HumanTrueFalseTrueFalseFalseFalseNaN70202021-11-23 18:55:29+00:002023-10-06 22:50:45+00:0002.0794420.0000001.0986120.000000
19767BotTrueFalseFalseFalseTrueFalseNaN100102016-04-22 22:11:59+00:002022-07-07 19:48:21+00:0002.3978950.0000000.6931470.000000